ForceNet builds on the force-centric GNS framework [1]}, [2]}, [3]}. Here a model's primary output is per-atom forces (thus, force-centric). The GNS framework follows three steps to predict forces: (1) A graph is constructed from 3D points, (2) an encoder GNN is applied to the graph to obtain node embeddings, and (3) a decoder is applied to the node embeddings to predict the per-node forces.
The GNS framework has been applied to relatively simple physical systems such as fluids, rigid body, and glassy systems, where ground-truth calculations are already cheap and can be performed on-the-fly during training.
Compared to these domains, using ML to approximate expensive quantum physics calculations is more practically impactful and challenging. Whether GNS is effective in the practically-relevant applications remains largely open.
As we demonstrate empirically, the off-the-shelf GNN model used in GNS fails to accurately predict the quantum mechanical forces, necessitating more careful design of model architectures.
